PNNL's Building Energy Model AI is an open-source bot that can help energy modelers save time and money.

Automated liquid handling, combined with data analysis and AI, optimizes microbiological growth conditions at PNNL.

PNNL set to create a practical path for near-term hybrid classical-quantum computers with NVIDIA accelerated computing.

PNNL addresses vulnerabilities interconnected to operational technologies by improving cybersecurity with targeted tools and trainings.

Connecting energy generation, electricity storage, and using sensors and control software to track load, including precommercial market marine energy generation technologies.

Emilie Purvine, a chief data scientist at PNNL, was elected as a council member at large of the American Mathematical Society.

Secretary Wright visited PNNL's campus in Richland, Washington, late last year to meet with innovators in chemistry, biology, computing, and more.

Darío Gil, DOE's Undersecretary for Science, makes his inaugural visit to PNNL.

A modeling study shows that adding batteries to a dam could decrease the wear and tear on hydropower turbines and open up new opportunities for dam operators to earn revenue.

Delivering excellence in power systems and electric grid advancement earns two PNNLers the association’s top distinction.

PNNL researchers Daniel Deng and Yehia Ibrahim join the 2025 class of National Academy of Inventors Fellows.

A new patent-pending technology can quickly identify refrigerant leakage in heating, cooling, and refrigeration systems.

The Genesis Mission will mobilize the Department of Energy’s 17 National Laboratories, industry, and academia to build an integrated discovery platform.

AI for Good event commemorates 60 years of community engagement, features AI applications, and offers practical AI tools to empower people.

PNNL’s SHAD-TAGS+ allows researchers to monitor fish species to help inform energy project development.

Micron and PNNL co-design the Crete computer system, delivering a new memory architecture for data-intensive scientific computing and AI applications.

This summer, scientists at PNNL led discussions on their latest research related to artificial intelligence and One Health at the Health and Environmental Sciences Institute conference.

PNNL showcased its expertise in AI, machine learning, and related domains at the International Conference on Machine Learning.

Aaron Luttman and Jonathan Forman represented PNNL at the high-profile "Risk and Reduction Science and Policy Forum" organized by Johns Hopkins University and supported by the Defense Threat Reduction Agency.

Recognizing visionary scientists for exceptional research, leadership, and impact at PNNL.